Summary Information technology companies currently use data mining techniques in different areas with the goal of increasing the quality of decision‐making and to improve their business performance. The study described in this paper uses a data mining approach to produce an effort estimation of a software development process. It is based on data collected in a Croatian information technology company. The study examined 27 software projects with a total effort exceeding 42 000 work hours. The presented model employs a modified Cross‐Industry Standard Process for Data Mining, where prior to model creation, additional clustering of projects is performed. The results generated by the proposed approach generally had a smaller effort estimation error than the results of human experts. The applied approach has proved that sound results can be gained through the use of data mining within the studied area. As a result, it would be wise to use such estimates as additional input in the decision‐making process.

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Software production is a complex process. Accurate estimation of the effort required to build the product, regardless of its type and applied methodology, is one of the key problems in the field of software engineering. This study presents the approach to effort estimation on agile software project using local data and data mining techniques, in particular k-nearest neighbor clustering algorithm. The applied process is iterative, meaning that in order to build predictive models, sets of data from previously executed project cycles are used. These models are then utilized to generate estimate for the next development cycle. Used data enrichment process, proved to be useful as results of effort prediction indicate decrease in estimation error compared to the estimates produced solely by the estimators. The proposed approach suggests that similar models can be built by other organizations as well, using the local data at hand and this way optimizing the management of the software product development.Povzetek: V prispevku je predstavljen pristop strojnega rudarjenja za modeliranje agilnih programskih projektov.

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This paper presents the research on reliability of high-speed radial marine diesel engine Zvijezda M 504 B2 based on experimental data of malfunctions on the engine and time required for repair. Exploitation reliability and failure intensity were calculated from operational data collected from the engine log book. Results were tested by computer program to determine the relevance of the obtained results. Mathematically calculated reliability model of high-speed radial marine diesel engine Zvijezda M 504 B2 showed continuous increasing function of the intensity of failure and the fact that the reliability of the engine can be reliably approximated by Weibull distribution. Based on the obtained results it has been shown that this distribution, regardless of its complexity, should be used in practice when calculating reliability of engines with similar constant and growing malfunction’s intensity. The conclusion is that proposed distribution enables a better depiction of the observed technical system and the impact of aging of components on the system reliability.

Turnover of the personnel represents a serious issue for management of software projects. The buildup of competences and phasing in of the people into the project requires both time and effort. This paper presents a case study of a large in-house agile software development project. The research goal was to determine the effects that turnover has on the expert effort estimation. In order to do this, paper examines relations across empirical data on a studied project. Study findings are the following: a) it is necessary to distinguish types of turnover, b) the general and planned turnover do not necessarily have a negative effect on estimation accuracy, and c) the unplanned turnover can have a significant negative impact on the reliability of the estimates and therefore should be treated with special attention. Results suggest that these facts should be taken into account both by the management and human resources.